Influenza H1N1 antigenic and sequence data (doi:10.21979/N9/O5XL2X)

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Document Description

Citation

Title:

Influenza H1N1 antigenic and sequence data

Identification Number:

doi:10.21979/N9/O5XL2X

Distributor:

DR-NTU (Data)

Date of Distribution:

2019-04-22

Version:

1

Bibliographic Citation:

Yin, Rui, 2019, "Influenza H1N1 antigenic and sequence data", https://doi.org/10.21979/N9/O5XL2X, DR-NTU (Data), V1

Study Description

Citation

Title:

Influenza H1N1 antigenic and sequence data

Identification Number:

doi:10.21979/N9/O5XL2X

Authoring Entity:

Yin, Rui (Nanyang Technological University)

Software used in Production:

Python

Grant Number:

AcRF Tier 2 grant MOE2014-T2-2-023

Distributor:

DR-NTU (Data)

Access Authority:

Yin Rui

Depositor:

Yin Rui

Date of Deposit:

2019-04-22

Holdings Information:

https://doi.org/10.21979/N9/O5XL2X

Study Scope

Keywords:

Computer and Information Science, Medicine, Health and Life Sciences, Computer and Information Science, Medicine, Health and Life Sciences, Influenza H1N1, Antigenic variant, Pandemics, Epidemics, Stacking model

Abstract:

H1N1 is the earliest emerging subtype of influenza A viruses with available genomic sequences, has caused several pandemics and seasonal epidemics, resulting in millions of deaths and enormous economic losses. Timely determination of new antigenic variants is crucial for the vaccine selection and flu prevention. In this study, we chronologically divided the H1N1 strains into several periods in terms of the epidemics and pandemics. Computational models have been constructed to predict antigenic variants based on epidemic and pandemic periods. By sequence analysis, we demonstrated the diverse mutation patterns of HA1 protein on different periods and that an individual model built upon each period can not represent the variations of H1N1 virus. A stacking model was established for the prediction of antigenic variants, combining all the variation patterns across periods, which would help assess a new influenza strain’s antigenicity. Three different feature extraction methods, i.e. residue-based, regional band-based and epitope region-based, were applied on the stacking model to verify its feasibility and robustness. The results showed the capability of determining antigenic variants prediction with accuracy as high as 0.908 which performed better than any of the single models. The prediction performance using the stacking model indicates clear distinctions of mutation patterns and antigenicity between epidemic and pandemic strains. It would also facilitate rapid determination of antigenic variants and influenza surveillance

Kind of Data:

.rar

Methodology and Processing

Sources Statement

Data Access

Other Study Description Materials

Related Publications

Citation

Identification Number:

10.1371/journal.pone.0207777

Bibliographic Citation:

Yin, R., Tran, V. H., Zhou, X., Zheng, J.,& Kwoh, C. K. (2018). Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model. PLOS ONE, 13(12), e0207777-.

Citation

Identification Number:

10356/155627

Bibliographic Citation:

Yin, R., Tran, V. H., Zhou, X., Zheng, J. & Kwoh, C. K. (2018). Predicting antigenic variants of H1N1 influenza virus based on epidemics and pandemics using a stacking model. PloS One, 13(12), e0207777-.

Other Study-Related Materials

Label:

data_stacking.rar

Text:

This is the first published version.

Notes:

application/x-rar-compressed